In this script we conduct the estimation for the measure_marginal approach for a single given env = besu.

PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.

Expected a result file besu_pg_marginal_full5_c50_step1_shuffle_50_4.csv.

programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))

results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]

all_envs = c(env)
measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
                     FROM results
                     INNER JOIN
                       programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)
##   opcode op_count sample_id run_id measure_total_time_ns  env program_id
## 1    ADD       27         0      0               7683054 besu     ADD_27
## 2    ADD       27         0      1               7921775 besu     ADD_27
## 3    ADD       27         0      2               7844004 besu     ADD_27
## 4    ADD       27         0      3               7794454 besu     ADD_27
## 5    ADD       27         0      4               7725708 besu     ADD_27
## 6    ADD       27         0      5               7929989 besu     ADD_27

Switch removed_outliers to FALSE to see the comparison.

boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))

if (removed_outliers) {
  measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
  mix_model = normalmixEM(df$measure_total_time_ns)
  print(summary(mix_model))
  plot(mix_model,which=2)
  mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
  mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
  over_threshold = which(df$measure_total_time_ns > mode_midpoint)
  df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
    
  return(df)
}

# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
  if (missing(bimodal_opcodes)) {
    bimodal_opcodes = c()
  }
  if (missing(plots)) {
    plots = "scatter"
  }
  if (missing(use_median)) {
    use_median = FALSE
  }
  print(c(opcode, env))
  
  df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
  
  if (opcode %in% bimodal_opcodes) {
    par(mfrow=c(1,2))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    title(main=paste(env, opcode))
    # correct_bimodal plots the second plot inside
    df = correct_bimodal(df)
  }
  
  if (use_median) {
    f = median
  } else {
    f = mean
  }
  df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
  
  model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
  print(summary(model_mean))
  slope = model_mean$coefficients[['op_count']]
  stderr = summary(model_mean)$coefficients['op_count','Std. Error']
  
  if (plots == "scatter" | plots == "all") {
    par(mfrow=c(1,1))
    boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
    rounded_slope = round(slope, 3)
    rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
    rounded_stderr = round(stderr, 3)
    title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
    abline(model_mean, col="red")
  }
  if (plots == "diagnostics" | plots == "all") {
    par(mfrow=c(2,2))
    plot(model_mean)
  }
  list("slope" = slope, "stderr" = stderr)
}

extract_opcodes <- function() {
  unique(measurements$opcode)
}
all_opcodes = extract_opcodes()

# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')

Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.

for (opcode in all_opcodes) {
  boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}

Now we can investigate the linear regressions.

if (env == 'evmone') {
  bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
  bimodals = c()
}

for (opcode in all_opcodes) {
  estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
  estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}
## [1] "ADD"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -52279 -10915  -2341   8374  37758 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3502078.1     4966.0   705.2 <0.0000000000000002 ***
## op_count      32687.8      171.2   191.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17990 on 49 degrees of freedom
## Multiple R-squared:  0.9987, Adjusted R-squared:  0.9986 
## F-statistic: 3.647e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MUL"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -55451 -10295    532  10691  69405 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3505621.5     5450.4   643.2 <0.0000000000000002 ***
## op_count     104188.9      187.9   554.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19750 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 3.076e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SUB"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -63734  -7823   -189  11288  42546 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3509751.3     4604.5   762.2 <0.0000000000000002 ***
## op_count      76324.5      158.7   480.9 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16680 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 2.313e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIV"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -73836  -8271   -787  11939  56189 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3506457.2     5571.7   629.3 <0.0000000000000002 ***
## op_count      31828.8      192.1   165.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20190 on 49 degrees of freedom
## Multiple R-squared:  0.9982, Adjusted R-squared:  0.9982 
## F-statistic: 2.747e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SDIV" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -300791  -54512  -10517   30828  298442 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3728227.5    27723.3  134.48 <0.0000000000000002 ***
## op_count      68287.9      955.6   71.46 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 100500 on 49 degrees of freedom
## Multiple R-squared:  0.9905, Adjusted R-squared:  0.9903 
## F-statistic:  5107 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MOD"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -302978  -55135  -11809   34289  282087 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3737434.7    28240.5  132.34 <0.0000000000000002 ***
## op_count      59731.7      973.4   61.36 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 102300 on 49 degrees of freedom
## Multiple R-squared:  0.9872, Adjusted R-squared:  0.9869 
## F-statistic:  3765 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SMOD" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -299344  -42996  -12236   25675  390088 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3730418      29134  128.04 <0.0000000000000002 ***
## op_count       59513       1004   59.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 105600 on 49 degrees of freedom
## Multiple R-squared:  0.9862, Adjusted R-squared:  0.986 
## F-statistic:  3512 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDMOD" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -351965  -66341  -15382   41796  397857 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3794320      33818  112.20 <0.0000000000000002 ***
## op_count       72659       1166   62.33 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 122500 on 49 degrees of freedom
## Multiple R-squared:  0.9875, Adjusted R-squared:  0.9873 
## F-statistic:  3885 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MULMOD" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -357883  -74380  -31088   58415  354104 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3823473      40151   95.23 <0.0000000000000002 ***
## op_count       76193       1384   55.05 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 145500 on 49 degrees of freedom
## Multiple R-squared:  0.9841, Adjusted R-squared:  0.9838 
## F-statistic:  3031 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EXP"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -215463  -46176     829   48189  153173 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3629179.0    22340.8   162.4 <0.0000000000000002 ***
## op_count     242949.4      770.1   315.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80950 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9995 
## F-statistic: 9.953e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SIGNEXTEND" "besu"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -57290 -11336   -255  12911  43952 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3516202.5     5592.3   628.8 <0.0000000000000002 ***
## op_count      97857.2      192.8   507.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20260 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 2.577e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "LT"   "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -60897 -10665   1403  12589  45577 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3492540.3     5617.3   621.7 <0.0000000000000002 ***
## op_count     107354.4      193.6   554.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20350 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 3.074e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GT"   "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -48256 -16250   3219  14508  39123 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3489786.8     5592.9   624.0 <0.0000000000000002 ***
## op_count     107475.8      192.8   557.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20270 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 3.108e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SLT"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -46201 -12266  -4127   9837  74141 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3508292       5569   629.9 <0.0000000000000002 ***
## op_count       23422        192   122.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20180 on 49 degrees of freedom
## Multiple R-squared:  0.9967, Adjusted R-squared:  0.9967 
## F-statistic: 1.489e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SGT"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -47859  -7333  -1205  10442  21816 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3504719.7     3910.5   896.2 <0.0000000000000002 ***
## op_count      23337.9      134.8   173.1 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14170 on 49 degrees of freedom
## Multiple R-squared:  0.9984, Adjusted R-squared:  0.9983 
## F-statistic: 2.998e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "EQ"   "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -32196  -7960   -746   9705  32817 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3486603.5     3913.0   891.0 <0.0000000000000002 ***
## op_count      74121.1      134.9   549.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14180 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 3.02e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ISZERO" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38002  -8311     88   8087  32255 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3489316.3     3949.8   883.4 <0.0000000000000002 ***
## op_count      40797.9      136.1   299.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14310 on 49 degrees of freedom
## Multiple R-squared:  0.9995, Adjusted R-squared:  0.9994 
## F-statistic: 8.98e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "AND"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -29464.6 -10019.7    783.5   9156.1  28637.0 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3489035.1     3584.2   973.4 <0.0000000000000002 ***
## op_count      75815.6      123.5   613.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 12990 on 49 degrees of freedom
## Multiple R-squared:  0.9999, Adjusted R-squared:  0.9999 
## F-statistic: 3.766e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "OR"   "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -63511  -8493    854  11278  54950 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3490237.8     5109.7   683.1 <0.0000000000000002 ***
## op_count      75651.6      176.1   429.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18510 on 49 degrees of freedom
## Multiple R-squared:  0.9997, Adjusted R-squared:  0.9997 
## F-statistic: 1.845e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "XOR"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44603  -8680   3459   8583  25998 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3491596.0     4019.6   868.6 <0.0000000000000002 ***
## op_count      75642.8      138.6   546.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14560 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 2.981e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NOT"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -28142  -9419  -1214   6787 108207 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3491108.3     5662.4   616.5 <0.0000000000000002 ***
## op_count      43165.7      195.2   221.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20520 on 49 degrees of freedom
## Multiple R-squared:  0.999,  Adjusted R-squared:  0.999 
## F-statistic: 4.891e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "BYTE" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -68704  -9613   3019  11994  32518 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3492218.9     4805.3   726.7 <0.0000000000000002 ***
## op_count      83253.8      165.6   502.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17410 on 49 degrees of freedom
## Multiple R-squared:  0.9998, Adjusted R-squared:  0.9998 
## F-statistic: 2.526e+05 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHL"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43553 -22684 -13333  -2226 428927 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3511025.9    19557.5   179.5 <0.0000000000000002 ***
## op_count      54400.7      674.1    80.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 70860 on 49 degrees of freedom
## Multiple R-squared:  0.9925, Adjusted R-squared:  0.9924 
## F-statistic:  6512 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SHR"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -114605  -30951  -18830   -7441  275285 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3537638.8    22131.3  159.85 <0.0000000000000002 ***
## op_count      57479.8      762.8   75.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 80190 on 49 degrees of freedom
## Multiple R-squared:  0.9914, Adjusted R-squared:  0.9913 
## F-statistic:  5677 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SAR"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -91931 -36025 -26901  -8980 374010 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3541944.8    25459.0   139.1 <0.0000000000000002 ***
## op_count      55724.5      877.5    63.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 92250 on 49 degrees of freedom
## Multiple R-squared:  0.988,  Adjusted R-squared:  0.9877 
## F-statistic:  4032 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ADDRESS" "besu"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -97606 -45265  -8562  39172 140926 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1515055.1    16066.1   94.30 <0.0000000000000002 ***
## op_count       9315.4      553.8   16.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 58210 on 49 degrees of freedom
## Multiple R-squared:  0.8524, Adjusted R-squared:  0.8494 
## F-statistic:   283 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "ORIGIN" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -77141 -40379  -5200  37134 105890 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1527943      13577  112.54 <0.0000000000000002 ***
## op_count        8140        468   17.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49190 on 49 degrees of freedom
## Multiple R-squared:  0.8606, Adjusted R-squared:  0.8578 
## F-statistic: 302.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLER" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -98256 -36489   -353  32388 105683 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1528833.9    13164.8  116.13 <0.0000000000000002 ***
## op_count       8799.1      453.8   19.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47700 on 49 degrees of freedom
## Multiple R-squared:  0.8847, Adjusted R-squared:  0.8824 
## F-statistic:   376 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLVALUE" "besu"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -79438 -46909 -19387  35630 192806 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1510745.9    16846.9   89.67 <0.0000000000000002 ***
## op_count       9111.1      580.7   15.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 61040 on 49 degrees of freedom
## Multiple R-squared:  0.834,  Adjusted R-squared:  0.8306 
## F-statistic: 246.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATALOAD" "besu"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -66946 -15154  -1713  14467  77817 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 5312799.3     7780.7   682.8 <0.0000000000000002 ***
## op_count      56491.2      268.2   210.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28190 on 49 degrees of freedom
## Multiple R-squared:  0.9989, Adjusted R-squared:  0.9989 
## F-statistic: 4.437e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATASIZE" "besu"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40675 -14772  -5206  -1023 196442 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1485551.7    10006.9  148.45 <0.0000000000000002 ***
## op_count      10008.6      344.9   29.02 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36260 on 49 degrees of freedom
## Multiple R-squared:  0.945,  Adjusted R-squared:  0.9439 
## F-statistic:   842 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CALLDATACOPY" "besu"        
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -171930  -57593  -16076   66450  152729 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3762493.1    22714.3  165.64 <0.0000000000000002 ***
## op_count      39900.6      782.9   50.96 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 82300 on 49 degrees of freedom
## Multiple R-squared:  0.9815, Adjusted R-squared:  0.9811 
## F-statistic:  2597 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODESIZE" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -42597 -13506  -2060   5355  79603 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1483679.0     5906.3  251.21 <0.0000000000000002 ***
## op_count      10206.8      203.6   50.14 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21400 on 49 degrees of freedom
## Multiple R-squared:  0.9809, Adjusted R-squared:  0.9805 
## F-statistic:  2514 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CODECOPY" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -163422  -72237    6348   65283  166196 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3702314.7    24353.4   152.0 <0.0000000000000002 ***
## op_count      40793.8      839.4    48.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 88240 on 49 degrees of freedom
## Multiple R-squared:  0.9797, Adjusted R-squared:  0.9793 
## F-statistic:  2362 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASPRICE" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -74252 -38229 -21211  41082 100190 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1528059.1    13648.2  111.96 <0.0000000000000002 ***
## op_count       8510.1      470.4   18.09 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49450 on 49 degrees of freedom
## Multiple R-squared:  0.8698, Adjusted R-squared:  0.8671 
## F-statistic: 327.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATASIZE" "besu"          
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -65413 -36503  -9150  31580 128331 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1513785.2    12883.8   117.5 <0.0000000000000002 ***
## op_count       9548.7      444.1    21.5 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 46680 on 49 degrees of freedom
## Multiple R-squared:  0.9042, Adjusted R-squared:  0.9022 
## F-statistic: 462.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "RETURNDATACOPY" "besu"          
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -244051  -98990    6665   85329  203531 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  9114953      33460  272.41 <0.0000000000000002 ***
## op_count       47436       1153   41.13 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 121200 on 49 degrees of freedom
## Multiple R-squared:  0.9718, Adjusted R-squared:  0.9713 
## F-statistic:  1692 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "COINBASE" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -83971 -34566  -5112  20151 103612 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1497668.8    13298.8  112.62 <0.0000000000000002 ***
## op_count       9323.5      458.4   20.34 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 48190 on 49 degrees of freedom
## Multiple R-squared:  0.8941, Adjusted R-squared:  0.8919 
## F-statistic: 413.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "TIMESTAMP" "besu"     
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -34352 -13233  -4492  10999  59492 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1487837.9     5531.5  268.98 <0.0000000000000002 ***
## op_count       9833.2      190.7   51.57 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20040 on 49 degrees of freedom
## Multiple R-squared:  0.9819, Adjusted R-squared:  0.9815 
## F-statistic:  2660 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "NUMBER" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -54000 -27378 -16441   5913 290466 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1517474.3    15776.4   96.19 <0.0000000000000002 ***
## op_count       9719.0      543.8   17.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57160 on 49 degrees of freedom
## Multiple R-squared:  0.867,  Adjusted R-squared:  0.8643 
## F-statistic: 319.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DIFFICULTY" "besu"      
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -66700 -44024 -11775  18498 128725 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1518159.3    15226.9   99.70 <0.0000000000000002 ***
## op_count       8890.9      524.9   16.94 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55170 on 49 degrees of freedom
## Multiple R-squared:  0.8541, Adjusted R-squared:  0.8512 
## F-statistic:   287 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GASLIMIT" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -57181 -21790 -10626   6531 128124 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1498426.8    10830.1  138.36 <0.0000000000000002 ***
## op_count       9973.0      373.3   26.72 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39240 on 49 degrees of freedom
## Multiple R-squared:  0.9358, Adjusted R-squared:  0.9344 
## F-statistic: 713.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "CHAINID" "besu"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -86959 -48662 -10408  38690 141022 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1537545.4    16485.7   93.27 <0.0000000000000002 ***
## op_count       8624.7      568.2   15.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 59730 on 49 degrees of freedom
## Multiple R-squared:  0.8246, Adjusted R-squared:  0.821 
## F-statistic: 230.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SELFBALANCE" "besu"       
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -830562  -12268   21371   63344  137793 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  2329844      38542   60.45 <0.0000000000000002 ***
## op_count      370962       1328  279.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 139700 on 49 degrees of freedom
## Multiple R-squared:  0.9994, Adjusted R-squared:  0.9994 
## F-statistic: 7.797e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "POP"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -195123 -103831  -19290   66653  610822 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1863264      40898   45.56 <0.0000000000000002 ***
## op_count       20733       1410   14.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 148200 on 49 degrees of freedom
## Multiple R-squared:  0.8153, Adjusted R-squared:  0.8115 
## F-statistic: 216.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MLOAD" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37804 -13698  -3373  13829  61963 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 5328581.2     5799.4   918.8 <0.0000000000000002 ***
## op_count      20836.6      199.9   104.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21010 on 49 degrees of freedom
## Multiple R-squared:  0.9955, Adjusted R-squared:  0.9954 
## F-statistic: 1.087e+04 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -184786  -50291  -19881   42908  169139 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3757432.9    23274.9   161.4 <0.0000000000000002 ***
## op_count      39314.1      802.3    49.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 84330 on 49 degrees of freedom
## Multiple R-squared:   0.98,  Adjusted R-squared:  0.9796 
## F-statistic:  2401 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSTORE8" "besu"   
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -188913  -51465  -20236   54883  218136 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3745267.5    24345.6  153.84 <0.0000000000000002 ***
## op_count      24316.7      839.2   28.98 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 88210 on 49 degrees of freedom
## Multiple R-squared:  0.9449, Adjusted R-squared:  0.9437 
## F-statistic: 839.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMP" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -45182 -25981  -7384  19685  85791 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1458714       9920  147.04 <0.0000000000000002 ***
## op_count       13647        342   39.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35950 on 49 degrees of freedom
## Multiple R-squared:  0.9702, Adjusted R-squared:  0.9695 
## F-statistic:  1593 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPI" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -91950 -17627  -1875  22934 180197 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3453745.6    11629.6   297.0 <0.0000000000000002 ***
## op_count      15073.5      400.9    37.6 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42140 on 49 degrees of freedom
## Multiple R-squared:  0.9665, Adjusted R-squared:  0.9658 
## F-statistic:  1414 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PC"   "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43561 -15380  -2953  10998 105786 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1492752.0     7813.5  191.05 <0.0000000000000002 ***
## op_count       9567.3      269.3   35.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28310 on 49 degrees of freedom
## Multiple R-squared:  0.9626, Adjusted R-squared:  0.9619 
## F-statistic:  1262 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "MSIZE" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -85306 -35745  -9146  41258  94993 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1532337.1    13129.2  116.71 <0.0000000000000002 ***
## op_count       9056.1      452.6   20.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47570 on 49 degrees of freedom
## Multiple R-squared:  0.891,  Adjusted R-squared:  0.8888 
## F-statistic: 400.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "GAS"  "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40580 -18235  -6048   5114 210899 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1494062.1    10343.1  144.45 <0.0000000000000002 ***
## op_count      10249.0      356.5   28.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37480 on 49 degrees of freedom
## Multiple R-squared:  0.944,  Adjusted R-squared:  0.9429 
## F-statistic: 826.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "JUMPDEST" "besu"    
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -20165.1  -7840.2   -364.2   7119.0  24590.7 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 233128.5     2995.8   77.82 <0.0000000000000002 ***
## op_count      7455.7      103.3   72.20 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 10850 on 49 degrees of freedom
## Multiple R-squared:  0.9907, Adjusted R-squared:  0.9905 
## F-statistic:  5213 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH1" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39231 -24421  -7196   8152  87366 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1488709.2     8916.3  166.97 <0.0000000000000002 ***
## op_count      10279.8      307.3   33.45 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32310 on 49 degrees of freedom
## Multiple R-squared:  0.958,  Adjusted R-squared:  0.9572 
## F-statistic:  1119 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH2" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -51031 -21474  -3245   9785 178301 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1501368.5     9977.1   150.5 <0.0000000000000002 ***
## op_count       9630.9      343.9    28.0 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36150 on 49 degrees of freedom
## Multiple R-squared:  0.9412, Adjusted R-squared:   0.94 
## F-statistic: 784.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH3" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -57534 -22934 -11582   3671 128700 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1499118.3    11841.3  126.60 <0.0000000000000002 ***
## op_count      10198.3      408.2   24.99 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42910 on 49 degrees of freedom
## Multiple R-squared:  0.9272, Adjusted R-squared:  0.9257 
## F-statistic: 624.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH4" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -39734 -22798 -10425   7013 214329 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1472698.3    11352.4  129.72 <0.0000000000000002 ***
## op_count      10858.8      391.3   27.75 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41130 on 49 degrees of freedom
## Multiple R-squared:  0.9402, Adjusted R-squared:  0.939 
## F-statistic: 770.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH5" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -54336 -27249 -11312  14319 119982 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1500548.4    11119.2  134.95 <0.0000000000000002 ***
## op_count       9979.7      383.3   26.04 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 40290 on 49 degrees of freedom
## Multiple R-squared:  0.9326, Adjusted R-squared:  0.9312 
## F-statistic:   678 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH6" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -55537 -19179  -5047   6888 101739 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1489670.9     9536.3  156.21 <0.0000000000000002 ***
## op_count      10360.8      328.7   31.52 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34550 on 49 degrees of freedom
## Multiple R-squared:  0.953,  Adjusted R-squared:  0.952 
## F-statistic: 993.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH7" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -61446 -34051  -9536  14684 210191 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1506749.9    13698.7  109.99 <0.0000000000000002 ***
## op_count      10051.4      472.2   21.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49640 on 49 degrees of freedom
## Multiple R-squared:  0.9024, Adjusted R-squared:  0.9004 
## F-statistic: 453.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH8" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44018 -22828  -8217   7107 140498 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1491647.8     9800.3  152.20 <0.0000000000000002 ***
## op_count      10137.3      337.8   30.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 35510 on 49 degrees of freedom
## Multiple R-squared:  0.9484, Adjusted R-squared:  0.9473 
## F-statistic: 900.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH9" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53101 -18305  -6946   6337 100329 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1496077.7     8184.2  182.80 <0.0000000000000002 ***
## op_count       9936.3      282.1   35.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29650 on 49 degrees of freedom
## Multiple R-squared:  0.962,  Adjusted R-squared:  0.9612 
## F-statistic:  1241 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH10" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -47220 -17604  -1448   9342  99198 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1496659.5     8146.8  183.71 <0.0000000000000002 ***
## op_count      10043.4      280.8   35.77 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 29520 on 49 degrees of freedom
## Multiple R-squared:  0.9631, Adjusted R-squared:  0.9624 
## F-statistic:  1279 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH11" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -49018 -23148  -2026   9028 144972 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1483763.7     9393.8  157.95 <0.0000000000000002 ***
## op_count      10430.8      323.8   32.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 34040 on 49 degrees of freedom
## Multiple R-squared:  0.9549, Adjusted R-squared:  0.954 
## F-statistic:  1038 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH12" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -56896 -26590 -12540  14560 169936 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1502261      12938  116.11 <0.0000000000000002 ***
## op_count       10030        446   22.49 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 46880 on 49 degrees of freedom
## Multiple R-squared:  0.9117, Adjusted R-squared:  0.9099 
## F-statistic: 505.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH13" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40152 -13960  -4360   7653  75255 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1481522.7     6808.5  217.60 <0.0000000000000002 ***
## op_count      10321.4      234.7   43.98 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 24670 on 49 degrees of freedom
## Multiple R-squared:  0.9753, Adjusted R-squared:  0.9748 
## F-statistic:  1934 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH14" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -63710 -18640  -8375   5222 175501 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1487054.9    10911.2  136.29 <0.0000000000000002 ***
## op_count      10287.4      376.1   27.35 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39540 on 49 degrees of freedom
## Multiple R-squared:  0.9385, Adjusted R-squared:  0.9373 
## F-statistic: 748.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH15" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53532 -31061 -17539  11755 266443 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1510426.5    15762.9   95.82 <0.0000000000000002 ***
## op_count       9980.0      543.3   18.37 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 57110 on 49 degrees of freedom
## Multiple R-squared:  0.8732, Adjusted R-squared:  0.8706 
## F-statistic: 337.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH16" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -52749 -23341  -4688  10760 196443 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1493909.1    11033.7  135.40 <0.0000000000000002 ***
## op_count      10147.6      380.3   26.68 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39980 on 49 degrees of freedom
## Multiple R-squared:  0.9356, Adjusted R-squared:  0.9343 
## F-statistic: 711.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH17" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -62287 -24752 -11024   9099 152387 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1483965.0    11740.2  126.40 <0.0000000000000002 ***
## op_count      10621.4      404.7   26.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 42540 on 49 degrees of freedom
## Multiple R-squared:  0.9336, Adjusted R-squared:  0.9322 
## F-statistic: 688.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH18" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -52968 -18171  -4718  13190  99155 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1497124.6     8924.3  167.76 <0.0000000000000002 ***
## op_count      10051.3      307.6   32.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 32340 on 49 degrees of freedom
## Multiple R-squared:  0.9561, Adjusted R-squared:  0.9552 
## F-statistic:  1068 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH19" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -58569 -27074 -13444   9963 173573 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1500107.4    12804.7  117.15 <0.0000000000000002 ***
## op_count      10078.2      441.4   22.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 46400 on 49 degrees of freedom
## Multiple R-squared:  0.9141, Adjusted R-squared:  0.9123 
## F-statistic: 521.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH20" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -53199 -23199 -11243  11745 264275 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1485322.2    13736.1  108.13 <0.0000000000000002 ***
## op_count      10450.8      473.5   22.07 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 49770 on 49 degrees of freedom
## Multiple R-squared:  0.9086, Adjusted R-squared:  0.9068 
## F-statistic: 487.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH21" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -55368 -25028 -13471   9088 143052 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1495836.4    11997.3  124.68 <0.0000000000000002 ***
## op_count      10112.5      413.5   24.45 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 43470 on 49 degrees of freedom
## Multiple R-squared:  0.9243, Adjusted R-squared:  0.9227 
## F-statistic:   598 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH22" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -52793 -18437  -6175   9081 148220 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1487811      10155  146.51 <0.0000000000000002 ***
## op_count       10406        350   29.73 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36790 on 49 degrees of freedom
## Multiple R-squared:  0.9475, Adjusted R-squared:  0.9464 
## F-statistic: 883.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH23" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -36054 -16464   -463   8861  77859 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1499232.9     6520.2  229.94 <0.0000000000000002 ***
## op_count       9850.0      224.7   43.83 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 23620 on 49 degrees of freedom
## Multiple R-squared:  0.9751, Adjusted R-squared:  0.9746 
## F-statistic:  1921 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH24" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44987 -24338 -11459   7711 161901 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1489749.9    10978.7  135.69 <0.0000000000000002 ***
## op_count      10484.9      378.4   27.71 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39780 on 49 degrees of freedom
## Multiple R-squared:   0.94,  Adjusted R-squared:  0.9388 
## F-statistic: 767.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH25" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -55140 -22484  -3919   8339 208132 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1493816.0    11475.3  130.18 <0.0000000000000002 ***
## op_count      10214.3      395.5   25.82 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 41580 on 49 degrees of freedom
## Multiple R-squared:  0.9316, Adjusted R-squared:  0.9302 
## F-statistic: 666.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH26" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -59709 -19520  -7189   8602  97079 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1515269.7    10255.4  147.75 <0.0000000000000002 ***
## op_count       9485.9      353.5   26.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37160 on 49 degrees of freedom
## Multiple R-squared:  0.9363, Adjusted R-squared:  0.935 
## F-statistic: 720.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH27" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -51577 -26431 -17466  -1291 435354 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1480819.0    19294.9   76.75 <0.0000000000000002 ***
## op_count      10794.2      665.1   16.23 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 69910 on 49 degrees of freedom
## Multiple R-squared:  0.8432, Adjusted R-squared:   0.84 
## F-statistic: 263.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH28" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -64972 -21444  -7446  14106 119394 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1509046.0    10817.9  139.50 <0.0000000000000002 ***
## op_count       9590.1      372.9   25.72 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 39200 on 49 degrees of freedom
## Multiple R-squared:  0.931,  Adjusted R-squared:  0.9296 
## F-statistic: 661.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH29" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -59256 -19057  -5733  16468  74979 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1482635.7     7390.9  200.60 <0.0000000000000002 ***
## op_count      10689.8      254.8   41.96 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 26780 on 49 degrees of freedom
## Multiple R-squared:  0.9729, Adjusted R-squared:  0.9724 
## F-statistic:  1761 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH30" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -48360 -20130  -5951  10287  95972 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1479733.2     7946.1  186.22 <0.0000000000000002 ***
## op_count      10685.7      273.9   39.01 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28790 on 49 degrees of freedom
## Multiple R-squared:  0.9688, Adjusted R-squared:  0.9682 
## F-statistic:  1522 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH31" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -41676 -27439 -13579   7200 154591 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1484080.2    12399.2  119.69 <0.0000000000000002 ***
## op_count      10617.1      427.4   24.84 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 44930 on 49 degrees of freedom
## Multiple R-squared:  0.9264, Adjusted R-squared:  0.9249 
## F-statistic: 617.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "PUSH32" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -65492 -23675  -2688  13288 121836 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1519434.9    10248.9  148.25 <0.0000000000000002 ***
## op_count       9665.5      353.3   27.36 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 37140 on 49 degrees of freedom
## Multiple R-squared:  0.9386, Adjusted R-squared:  0.9373 
## F-statistic: 748.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP1" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -25801 -10766  -3931   8029  62981 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3442906.7     4943.0  696.53 <0.0000000000000002 ***
## op_count       9825.2      170.4   57.67 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 17910 on 49 degrees of freedom
## Multiple R-squared:  0.9855, Adjusted R-squared:  0.9852 
## F-statistic:  3325 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP2" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35096 -13964  -2306  12303  55312 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3447794.2     5055.0  682.05 <0.0000000000000002 ***
## op_count       9906.9      174.2   56.86 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18320 on 49 degrees of freedom
## Multiple R-squared:  0.9851, Adjusted R-squared:  0.9848 
## F-statistic:  3233 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP3" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -44475 -20348  -4020   3815 295052 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3452560.7    13023.8  265.10 <0.0000000000000002 ***
## op_count       9988.6      448.9   22.25 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 47190 on 49 degrees of freedom
## Multiple R-squared:  0.9099, Adjusted R-squared:  0.9081 
## F-statistic: 495.1 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP4" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -33441 -10854   -891  10290  97391 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3443469.6     5848.8   588.8 <0.0000000000000002 ***
## op_count      10020.7      201.6    49.7 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 21190 on 49 degrees of freedom
## Multiple R-squared:  0.9806, Adjusted R-squared:  0.9802 
## F-statistic:  2471 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP5" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
##  -61663  -37491  -30644  -14776 1345295 
## 
## Coefficients:
##             Estimate Std. Error t value             Pr(>|t|)    
## (Intercept)  3475926      53791  64.619 < 0.0000000000000002 ***
## op_count        9526       1854   5.138           0.00000481 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 194900 on 49 degrees of freedom
## Multiple R-squared:  0.3501, Adjusted R-squared:  0.3368 
## F-statistic:  26.4 on 1 and 49 DF,  p-value: 0.000004814

## [1] "DUP6" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -56134  -9156  -2424  14362  33387 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3443568.3     4978.1   691.7 <0.0000000000000002 ***
## op_count       9849.7      171.6    57.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18040 on 49 degrees of freedom
## Multiple R-squared:  0.9853, Adjusted R-squared:  0.985 
## F-statistic:  3295 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP7" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -35337 -12671   2008  10266  55821 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  3466946       5078   682.7 <0.0000000000000002 ***
## op_count       10047        175    57.4 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18400 on 49 degrees of freedom
## Multiple R-squared:  0.9853, Adjusted R-squared:  0.985 
## F-statistic:  3294 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP8" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -50292 -18946  -5360   4074 364459 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3492118.1    15299.9  228.24 <0.0000000000000002 ***
## op_count      10171.1      527.4   19.29 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 55440 on 49 degrees of freedom
## Multiple R-squared:  0.8836, Adjusted R-squared:  0.8812 
## F-statistic:   372 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP9" "besu"
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -43173 -14314  -1954   9056 139525 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3444313.7     7763.9  443.63 <0.0000000000000002 ***
## op_count       9917.3      267.6   37.06 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 28130 on 49 degrees of freedom
## Multiple R-squared:  0.9655, Adjusted R-squared:  0.9648 
## F-statistic:  1373 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP10" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -45362 -11402  -2309   9626  54325 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3450140.4     5649.3   610.7 <0.0000000000000002 ***
## op_count       9775.8      194.7    50.2 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20470 on 49 degrees of freedom
## Multiple R-squared:  0.9809, Adjusted R-squared:  0.9805 
## F-statistic:  2520 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP11" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37377 -11627  -4189  10525  60547 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3520456.8     5036.9  698.93 <0.0000000000000002 ***
## op_count      10310.3      173.6   59.38 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 18250 on 49 degrees of freedom
## Multiple R-squared:  0.9863, Adjusted R-squared:  0.986 
## F-statistic:  3527 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP12" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -40729 -12739   1660   9667  36309 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3440531.8     3961.1  868.57 <0.0000000000000002 ***
## op_count      10094.2      136.5   73.93 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14350 on 49 degrees of freedom
## Multiple R-squared:  0.9911, Adjusted R-squared:  0.9909 
## F-statistic:  5466 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP13" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -58951 -28302 -12532   8985 516321 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3442568.7    21537.5  159.84 <0.0000000000000002 ***
## op_count      11290.2      742.4   15.21 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 78040 on 49 degrees of freedom
## Multiple R-squared:  0.8252, Adjusted R-squared:  0.8216 
## F-statistic: 231.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP14" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -41808 -14007   -507   6993 225773 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3469582.1    10050.8  345.20 <0.0000000000000002 ***
## op_count      10006.8      346.4   28.88 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 36420 on 49 degrees of freedom
## Multiple R-squared:  0.9445, Adjusted R-squared:  0.9434 
## F-statistic: 834.3 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP15" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -38695 -12769   -846  13187  40955 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3431051.0     4630.3  741.01 <0.0000000000000002 ***
## op_count      10242.8      159.6   64.18 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 16780 on 49 degrees of freedom
## Multiple R-squared:  0.9882, Adjusted R-squared:  0.988 
## F-statistic:  4119 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "DUP16" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -37688  -9731   -478  11319  42984 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 3588643.6     5286.2  678.87 <0.0000000000000002 ***
## op_count       9965.8      182.2   54.69 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 19150 on 49 degrees of freedom
## Multiple R-squared:  0.9839, Adjusted R-squared:  0.9836 
## F-statistic:  2991 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP1" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -181296  -93101  -16912   83599  200933 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1863654      29974   62.17 <0.0000000000000002 ***
## op_count       23659       1033   22.90 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108600 on 49 degrees of freedom
## Multiple R-squared:  0.9145, Adjusted R-squared:  0.9128 
## F-statistic: 524.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP2" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -177611  -88786  -12901   84691  212435 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1872646      30310   61.78 <0.0000000000000002 ***
## op_count       23452       1045   22.45 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 109800 on 49 degrees of freedom
## Multiple R-squared:  0.9114, Adjusted R-squared:  0.9096 
## F-statistic: 503.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP3" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -156513  -89011  -10280   79202  203654 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1865922      30198   61.79 <0.0000000000000002 ***
## op_count       23799       1041   22.86 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 109400 on 49 degrees of freedom
## Multiple R-squared:  0.9143, Adjusted R-squared:  0.9125 
## F-statistic: 522.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP4" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -201256  -75574  -29902   75160  206025 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1874799      29664   63.20 <0.0000000000000002 ***
## op_count       23382       1022   22.87 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 107500 on 49 degrees of freedom
## Multiple R-squared:  0.9143, Adjusted R-squared:  0.9126 
## F-statistic: 522.9 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP5" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -177788  -87287  -24681   80487  196629 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1879756      29348   64.05 <0.0000000000000002 ***
## op_count       23166       1012   22.90 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106300 on 49 degrees of freedom
## Multiple R-squared:  0.9145, Adjusted R-squared:  0.9128 
## F-statistic: 524.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP6" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -189494  -81037   -5955   68950  201945 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1892192      30336   62.38 <0.0000000000000002 ***
## op_count       23409       1046   22.39 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 109900 on 49 degrees of freedom
## Multiple R-squared:  0.9109, Adjusted R-squared:  0.9091 
## F-statistic: 501.2 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP7" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -202321  -76853  -11091   81967  201991 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1902890      29931   63.58 <0.0000000000000002 ***
## op_count       23840       1032   23.11 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108500 on 49 degrees of freedom
## Multiple R-squared:  0.9159, Adjusted R-squared:  0.9142 
## F-statistic:   534 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP8" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -187989  -79170  -19634   90768  223166 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1869643      29851   62.63 <0.0000000000000002 ***
## op_count       23572       1029   22.91 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108200 on 49 degrees of freedom
## Multiple R-squared:  0.9146, Adjusted R-squared:  0.9129 
## F-statistic: 524.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP9" "besu" 
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -190589  -81888   -9541   90663  197708 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1886731      30005   62.88 <0.0000000000000002 ***
## op_count       23022       1034   22.26 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 108700 on 49 degrees of freedom
## Multiple R-squared:   0.91,  Adjusted R-squared:  0.9082 
## F-statistic: 495.5 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP10" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -192024  -83509   -8871   74270  190474 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 1974936.8    27441.8   71.97 <0.0000000000000002 ***
## op_count      22906.0      945.9   24.22 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 99430 on 49 degrees of freedom
## Multiple R-squared:  0.9229, Adjusted R-squared:  0.9213 
## F-statistic: 586.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP11" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -187518  -87495  -34970   86269  595068 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1882072      38203   49.27 <0.0000000000000002 ***
## op_count       23400       1317   17.77 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 138400 on 49 degrees of freedom
## Multiple R-squared:  0.8657, Adjusted R-squared:  0.8629 
## F-statistic: 315.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP12" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -162779  -91739  -15715   85245  196744 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1913089      29338   65.21 <0.0000000000000002 ***
## op_count       22895       1011   22.64 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 106300 on 49 degrees of freedom
## Multiple R-squared:  0.9127, Adjusted R-squared:  0.911 
## F-statistic: 512.6 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP13" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -204574  -80769    1002   86188  217865 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1884096      31285   60.22 <0.0000000000000002 ***
## op_count       23471       1078   21.77 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 113400 on 49 degrees of freedom
## Multiple R-squared:  0.9063, Adjusted R-squared:  0.9043 
## F-statistic: 473.7 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP14" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -198397  -92042  -36789  102726  464604 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1930287      35748   54.00 <0.0000000000000002 ***
## op_count       21760       1232   17.66 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 129500 on 49 degrees of freedom
## Multiple R-squared:  0.8642, Adjusted R-squared:  0.8614 
## F-statistic: 311.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP15" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -163281  -90578   -7210   82103  185135 
## 
## Coefficients:
##              Estimate Std. Error t value            Pr(>|t|)    
## (Intercept) 2024177.2    28196.5   71.79 <0.0000000000000002 ***
## op_count      22945.6      971.9   23.61 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 102200 on 49 degrees of freedom
## Multiple R-squared:  0.9192, Adjusted R-squared:  0.9175 
## F-statistic: 557.4 on 1 and 49 DF,  p-value: < 0.00000000000000022

## [1] "SWAP16" "besu"  
## 
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -223032  -86075  -21051   75833  502957 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)  1967414      35739   55.05 <0.0000000000000002 ***
## op_count       23040       1232   18.70 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 129500 on 49 degrees of freedom
## Multiple R-squared:  0.8771, Adjusted R-squared:  0.8746 
## F-statistic: 349.8 on 1 and 49 DF,  p-value: < 0.00000000000000022

Export the results

write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)